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Found 8 papers, 2 papers with code
Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model
To address this issue, we compare the performance of a foundational Vision Transformer (ViT) model with a baseline Conditional Generative Adversarial Network (CGAN) model for missing value imputation in time series of multispectral satellite imagery.
Foundation Models for Generalist Geospatial Artificial Intelligence
This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive geospatial data.
GEO-Bench: Toward Foundation Models for Earth Monitoring
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks.
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.
LandCoverNet: A global benchmark land cover classification training dataset
Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals.
Generating Synthetic Multispectral Satellite Imagery from Sentinel-2
Multi-spectral satellite imagery provides valuable data at global scale for many environmental and socio-economic applications.
Semantic Segmentation of Medium-Resolution Satellite Imagery using Conditional Generative Adversarial Networks
The generalization property of CNN is poor for satellite imagery because the data can be very diverse in terms of landscape types, image resolutions, and scarcity of labels for different geographies and seasons.
Proceedings of the ICLR Workshop on Computer Vision for Agriculture (CV4A) 2020
The workshop was held in conjunction with the International Conference on Learning Representations (ICLR) 2020.
